Computer-aided detection (CAD) refers to the use of computers to analyze medical images to detect anatomical abnormalities therein. Sometimes used interchangeably with the term computer-aided detection are the terms computer-aided diagnosis, computer-assisted diagnosis, or computer-assisted detection. The outputs of CAD systems are sets of information sufficient to communicate the locations of anatomical abnormalities, or lesions, in a medical image, and can also include other information such as the type of lesion, degree of suspiciousness, and the like. Such CAD detections are most often communicated in the form of graphical annotations overlaid upon diagnostic-quality and/or reduced-resolution versions of the medical image. CAD results are mainly used by radiologists as “secondary reads” or secondary diagnosis tools. Some CAD implementations, however, have used CAD results in a “concurrent reading” context in which the radiologists look at the CAD results at the same time that they look at the images.
While CAD algorithms have been proposed and developed for a variety of different medical imaging modalities, much of the pioneering development in CAD technology was performed for the particular modality of x-ray mammography. X-ray mammography CAD systems are described, for example, in U.S. Pat. No. 5,729,620, U.S. Pat. No. 5,815,591, U.S. Pat. No. 5,917,929, U.S. Pat. No. 6,014,452, U.S. Pat. No. 6,075,879, U.S. Pat. No. 6,301,378, U.S. Pat. No. 6,574,357, and U.S. Pat. No. 6,909,795, each of which is incorporated by reference herein. Thousands of CAD systems for x-ray mammography are now installed worldwide, and are used to assist radiologists in the interpretation of millions of mammograms per year. Substantial effort and attention has been directed to improving the performance capabilities of CAD systems, such as x-ray mammography CAD systems. As known in the art, the performance of a CAD system can be characterized in terms of the interplay between its sensitivity (true positive rate) and specificity (true negative rate) as expressed, for example, in a free receiver operating characteristic (FROC) curve that plots system sensitivity versus the number of false marks per case. Higher performance, more effective CAD systems are characterized by a higher FROC curve, meaning that for any particular operating point at which the CAD system exhibits a particular number of false marks per case, there is a higher sensitivity, and, conversely, that for any particular operating point at which the CAD system exhibits a particular sensitivity, there is a lower number of false marks per case. The operating point at which a CAD system operates along its FROC curve is usually set by an internal numerical threshold that dictates how suspicious a particular finding must be, in terms of a computed internal numerical probability metric, in order to qualify as a “marked” finding on the user display. Although the operating point is often user-adjustable in modern CAD systems, which allows the user to choose their own desired trade-off point along the FROC curve, the actual FROC curve itself is fixed for any particular CAD system, and serves as an indication of the overall effectiveness of that CAD system.
One known method for improving the effectiveness of a CAD system is described in U.S. Pat. No. 6,067,372 to Gur, et. al. (hereinafter “Gur”), which is incorporated by reference herein, and involves processing each medical image using two or more independent CAD algorithms to generate two or more sets of CAD detections, followed by applying a statistical computation process to the two or more sets of CAD detections to generate a final set of CAD detections. An experiment is described in Gur in which 428 images having 220 verified masses were separately processed using two independent CAD schemes, with the results being combined using different statistical methods (ANDing and ORing), and in either case it was found that the combined technique provided significant improvement over either CAD method alone. Although not universally applied, the term “voting” is often used in the literature to describe the statistical computation process to the two or more sets of CAD detections. The use of voting in mammography processing is also described in US 2009/0129656A1, which is incorporated by reference herein.
As used herein, two CAD algorithms are termed independent if their separate application to the same image, or to the same population of images, yields at least one finding that appears differently in the two CAD result sets, either in a “binary” sense (marker on/marker off) or an “analog” sense (different internal probability metric). The particular extent to which two CAD algorithms are independent can vary from a high degree of independence, in which case the term “orthogonal” might more specifically characterize their relationship, to a low degree of independence, in which case the term “uncorrelated” or “partially uncorrelated” might more specifically characterize their relationship. By way of example, a relatively high degree of independence would probably be present if the two CAD algorithms were conceived, designed, and coded by two different teams of researchers located in different states or countries. In contrast, a relatively low degree of independence would probably be present if the two CAD algorithms were based on a common set of starting code, but were differently “tweaked” relative to certain feature computations, internal parameters, internal image filtering algorithms, and the like.
By way of example, as the term independent is used herein, the commonly assigned US 2009/0136113A1, supra, represents an example of the use of multiple independent CAD algorithms and a voting process. In US 2009/0136113A1, there is disclosed a common “foundational” CAD algorithm that is characterized by at least one of non-shift-invariance (i.e., the findings can at least partially change if the input image is shifted), non-rotational-invariance (i.e., the findings can at least partially change if the input image is rotated), and non-inversional-invariance (i.e., the findings can at least partially change if the input image is flipped). The foundational CAD algorithm is applied separately to differently shifted, rotated, and/or inverted versions of the same medical image, and the different CAD result sets voted upon to determine a final CAD result set. As the term independent is used herein, each application of such foundational CAD algorithm to the differently shifted, rotated, and/or inverted versions of the same medical image represents an instance of an independent CAD algorithm. By way of further example, Gur, supra, describes embodiments in which the same CAD algorithm is re-applied to a same medical image after different amounts of noise are added to that image, or the image is redigitized, and then a statistical computation (i.e., voting) process is applied to the different result sets to generate a final decision for each finding. As the term independent is used herein, each application of the CAD algorithm to a differently noise-perturbed or redigitized version of the same medical image represents an instance of an independent CAD algorithm.
Although the use of multiple independent CAD algorithms and voting processes has been proposed, issues arise in the practical application of this concept to improve the performance of CAD systems in practical clinical environments. Such issues are especially pronounced in the field of x-ray mammography CAD, in which case there is a large installed base of x-ray mammography CAD systems in clinics around the world around which different clinical practices, procedures, and expectations have been established. It would be desirable to harness the advantages of using multiple independent CAD algorithms and voting processes in a way that does not require an en masse deconstruction and reconstruction of systems and processes that would otherwise be associated with such a large paradigm shift. It would be desirable to integrate the use of multiple independent CAD algorithms and voting processes into the existing x-ray mammography CAD infrastructure in a way that does not upset the established clinical practices, procedures, and expectations that have already been developed, both in terms of the technology institutions that have shaped the current CAD landscape and the real-world clinical environments that have used CAD technology to save lives. Similar CAD-related issues exist for medical imaging modalities other than x-ray mammography, and still other issues arise as would be apparent to a person skilled in the art in view of the present disclosure.